计算机工程与应用2019，Vol.55Issue(23)：7-14,63,9.DOI:10.3778/j.issn.1002-8331.1908-0347
K-Means聚类算法研究综述
Survey on K-Means Clustering Algorithm
摘要
Abstract
The K-Means algorithm is a partition-based algorithm in cluster analysis. With an unsupervised learning algo-rithm, its advantages of simple thinking, good effect and easy implementation are widely used in fields such as machine learning. But the K-Means algorithm also has certain limitations. For example, the K number of clusters in the algorithm is difficult to determine how to choose the initial cluster center, how to detect and remove outliers and the distance and similarity measure. This paper summarizes the improvement of K-Means algorithm from several aspects, and compares it with the classical K-Means algorithm. In addition, it analyzes the advantages and disadvantages of the improved algo-rithm, and points out the problems. Finally, the development direction and trend of K-Means algorithm are prospected.关键词
K-Means/聚类算法/聚类中心/离群点Key words
K-Means/clustering algorithm/cluster center/outliers分类
信息技术与安全科学引用本文复制引用
杨俊闯,赵超..K-Means聚类算法研究综述[J].计算机工程与应用,2019,55(23):7-14,63,9.基金项目
河北省高等学校科学技术研究项目(No.QN2018109). （No.QN2018109）
